most citedParameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.CL2025

Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation

Jiayu Yao, Shenghua Liu, Yiwei Wang +5

Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustne…

cs.GR2025

STDR: Spatio-Temporal Decoupling for Real-Time Dynamic Scene Rendering

Zehao Li, Hao Jiang, Yujun Cai +7

Although dynamic scene reconstruction has long been a fundamental challenge in 3D vision, the recent emergence of 3D Gaussian Splatting (3DGS) offers a promising direction by enabl…

cs.CL20251 cited

Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

Baolong Bi, Shenghua Liu, Yiwei Wang +4

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and…

cs.CV2024

GradiSeg: Gradient-Guided Gaussian Segmentation with Enhanced 3D Boundary Precision

Zehao Li, Wenwei Han, Yujun Cai +5

While 3D Gaussian Splatting enables high-quality real-time rendering, existing Gaussian-based frameworks for 3D semantic segmentation still face significant challenges in boundary…

cs.CL2024

Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities

Baolong Bi, Shenghua Liu, Yiwei Wang +4

The parametric knowledge memorized by large language models (LLMs) becomes outdated quickly. In-context editing (ICE) is currently the most effective method for updating the knowle…